Analyzing student notes and questions to create personalized study guides

In the foreseeable future it will be technically possible for instructors, advisors and other delegated representatives of a college or university to access student participation and performance data in near-real time. One potential benefit of this increased data flow could include an improved ability to identify students at risk of academic failure or withdrawal. The availability of these data could also lead to creation of new adaptive learning measures that can automatically provide students personalized guidance. This demonstration will describe how the student notes and questions are being mined to provide student study guides that automatically link to outside resources. The demonstration will also report on how these new study guides have been received by the students and how they are at least partially responsible for a significant increase in student outcomes.